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Neural Comput. 2019 Oct;31(10):1945-1963. doi: 10.1162/neco_a_01219. Epub 2019 Aug 8.

A Minimum Free Energy Model of Motor Learning.

Author information

1
Department of Computer Science, University of California, Santa Barbara, Santa Barbara, CA 931056, U.S.A. brian_a_mitchell@engineering.ucsb.edu.
2
Human Research and Engineering Directorate, The CCDC Army Research Laboratory, Aberdeen Proving Ground, MD 21005, U.S.A., and Annenberg School for Communication, University of Pennsylvania, Philadelphia, PA 19104, U.S.A. nina.lauharatanahirun.civ@mail.mil.
3
Human Research and Engineering Directorate, The CCDC Army Research Laboratory, Aberdeen Proving Ground, MD 21005, U.S.A., and Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, U.S.A. javier.o.garcia.civ@mail.mil.
4
Department of Physical Medicine and Rehabilitation, Johns Hopkins Medical Institution, Baltimore, MD 21205, U.S.A. nwymbs@gmail.com.
5
Department of Psychological Brain Sciences, University of California, Santa Barbara, Santa Barbara, CA 931056, U.S.A. stgrafton@ucsb.edu.
6
Department of Psychological Brain Sciences, University of California, Santa Barbara, Santa Barbara, CA 931056, U.S.A.; Human Research and Engineering Directorate, The CCDC Army Research Laboratory, Aberdeen Proving Ground, MD 21005, U.S.A.; and Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, U.S.A. jean.m.vettel.civ@mail.mil.
7
Department of Computer Science and Department of Mechanical Engineering, University of California, Santa Barbara, Santa Barbara, CA 931056, U.S.A. petzold@engineering.ucsb.edu.

Abstract

Even highly trained behaviors demonstrate variability, which is correlated with performance on current and future tasks. An objective of motor learning that is general enough to explain these phenomena has not been precisely formulated. In this six-week longitudinal learning study, participants practiced a set of motor sequences each day, and neuroimaging data were collected on days 1, 14, 28, and 42 to capture the neural correlates of the learning process. In our analysis, we first modeled the underlying neural and behavioral dynamics during learning. Our results demonstrate that the densities of whole-brain response, task-active regional response, and behavioral performance evolve according to a Fokker-Planck equation during the acquisition of a motor skill. We show that this implies that the brain concurrently optimizes the entropy of a joint density over neural response and behavior (as measured by sampling over multiple trials and subjects) and the expected performance under this density; we call this formulation of learning minimum free energy learning (MFEL). This model provides an explanation as to how behavioral variability can be tuned while simultaneously improving performance during learning. We then develop a novel variant of inverse reinforcement learning to retrieve the cost function optimized by the brain during the learning process, as well as the parameter used to tune variability. We show that this population-level analysis can be used to derive a learning objective that each subject optimizes during his or her study. In this way, MFEL effectively acts as a unifying principle, allowing users to precisely formulate learning objectives and infer their structure.

PMID:
31393824
DOI:
10.1162/neco_a_01219

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